PDF(Pubmed)
Abstract：
BACKGROUND: The detection and management of intracranial aneurysms (IAs) are vital to prevent life-threatening complications like subarachnoid hemorrhage (SAH). Artificial Intelligence (AI) can analyze medical images, like CTA or MRA, spotting nuances possibly overlooked by humans. Early detection facilitates timely interventions and improved outcomes. Moreover, AI algorithms offer quantitative data on aneurysm attributes, aiding in long-term monitoring and assessing rupture risks.
METHODS: We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field.
RESULTS: Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives.
CONCLUSIONS: The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.
METHODS: We screened four databases (PubMed, Web of Science, IEEE and Scopus) for studies using artificial intelligence algorithms to identify IA. Based on algorithmic methodologies, we categorized them into classification, segmentation, detection and combined, and then their merits and shortcomings are compared. Subsequently, we elucidate potential challenges that contemporary algorithms might encounter within real-world clinical diagnostic contexts. Then we outline prospective research trajectories and underscore key concerns in this evolving field.
RESULTS: Forty-seven studies of IA recognition based on AI were included based on search and screening criteria. The retrospective results represent that current studies can identify IA in different modal images and predict their risk of rupture and blockage. In clinical diagnosis, AI can effectively improve the diagnostic accuracy of IA and reduce missed detection and false positives.
CONCLUSIONS: The AI algorithm can detect unobtrusive IA more accurately in communicating arteries and cavernous sinus arteries to avoid further expansion. In addition, analyzing aneurysm rupture and blockage before and after surgery can help doctors plan treatment and reduce the uncertainties in the treatment process.
摘要：
背景：颅内动脉瘤（IA）的检测和管理对于预防蛛网膜下腔出血（SAH）等危及生命的并发症至关重要。人工智能（AI）可以分析医学图像，比如CTA或MRA,发现人类可能忽视的细微差别。早期发现有助于及时干预和改善结果。此外，人工智能算法提供动脉瘤属性的定量数据，帮助长期监测和评估破裂风险。
方法：我们筛选了四个数据库(PubMed，WebofScience,IEEE和Scopus）用于使用人工智能算法识别IA的研究。基于算法方法，我们把它们分类，分割，检测和组合，然后比较了它们的优点和缺点。随后，我们阐明了当代算法在现实世界的临床诊断环境中可能遇到的潜在挑战.然后，我们概述了前瞻性研究轨迹，并强调了这一不断发展的领域中的关键问题。
结果：根据搜索和筛选标准，纳入了47项基于AI的IA识别研究。回顾性结果表明，当前的研究可以在不同的模态图像中识别IA，并预测其破裂和阻塞的风险。在临床诊断中,AI可有效提高IA的诊断准确率,减少漏检和假阳性。
结论：AI算法可以更准确地检测交通动脉和海绵窦动脉中的非突发性IA，以避免进一步扩张。此外,术前和术后分析动脉瘤破裂和阻塞可以帮助医生制定治疗方案，减少治疗过程中的不确定性。
方法：我们筛选了四个数据库(PubMed，WebofScience,IEEE和Scopus）用于使用人工智能算法识别IA的研究。基于算法方法，我们把它们分类，分割，检测和组合，然后比较了它们的优点和缺点。随后，我们阐明了当代算法在现实世界的临床诊断环境中可能遇到的潜在挑战.然后，我们概述了前瞻性研究轨迹，并强调了这一不断发展的领域中的关键问题。
结果：根据搜索和筛选标准，纳入了47项基于AI的IA识别研究。回顾性结果表明，当前的研究可以在不同的模态图像中识别IA，并预测其破裂和阻塞的风险。在临床诊断中,AI可有效提高IA的诊断准确率,减少漏检和假阳性。
结论：AI算法可以更准确地检测交通动脉和海绵窦动脉中的非突发性IA，以避免进一步扩张。此外,术前和术后分析动脉瘤破裂和阻塞可以帮助医生制定治疗方案，减少治疗过程中的不确定性。
